Context Inertia: When Agents Won’t Let Go

Persistent models have a persistence problem. It’s been a bit since we’ve added a new context failure mode, but the increasing persistence of frontier models has brought this fail to the fore. This one I call Context Inertia: Context Inertia is when the model commits to a hypothesis, subtask, or goal in its context and keeps pursuing it after the evidence says to stop or change course. This failure mode is part context poisoning (when an error makes it into the context and is repeatedly referenced) and part context distraction (when a context grows so long the model over-focuses on it), amplified by frontier models the labs have trained to be relentless instruction followers. A couple days ago, buried in a piece about AI self-replication capabilities, Anthropic shared a perfect Context Inertia case study when describing a custom harness: Memento is designed to mitigate a common failure mode we observe in long-horizon runs: an agent can become fixated on an incorrect idea (for, say,…